My seminar at the “Groupement de recherche”

As part of the Non-Conventional Optical Imaging Days (JIONC) of the Groupement de recherche IASIS, a science front consists of the joint design of optics and the sensor to reconstruct quality images. In this webinar, the optical part also includes memory aspects that enable all-optical processing that goes beyond just the image. In this presentation I discuss the interface of machine learning, statistical physics and wave optics in a scattering medium. It offers deep learning hardware architectures based on micro-mirrors addressed DMD (Digital Micro Device) and very efficient from an energy point of view.

Link to the seminar: https://www.canal-u.tv/chaines/gdr-iasis/seminaire-de-marco-leonetti-memory-light-spin-glasses-and-deep-classification

Photonic Stochastic Emergent Learning

We are in the middle of the AI revolution and novel hardware to support this disruptive software is highly sought. In a recent paper appeared on Nature Communications we propose a new, brain inspired, photonic based, lighting fast platform on which perform Deep Classification: the keystone on which many AIs are built on.

The Photonic Stochastic Emergent Learning is based on an evolution of the Emergent archetype, a paradigm which explains how memory elements are built in human brain starting from imperfect examples.

Our platform has several advantages with respect to previous solutions: it is easier to train and less energy consumptive.

This result is a joint effort of three big Italian research institution: il Consiglio Nazionale delle Ricerche (Nanotech institute), l’Istituto Italiano di Tecnologia e l’Università di Roma Sapienza, in collaboration with the Rebel Dynamics company, a beautiful example of public/private synergy.

I greatly thanks the coauthors of this concentrated effort: Prof. Giancarlo Ruocco and the CNR Researcher Giorgio Gosti.

Physic-Informed Denoising

Neural Networks NN are revolutionizing both everyday life and cutting edge research. A new trend is that of physically informed training, in which the extensive power of NN, embodied the a huge number of parameters employed, comes together with a physical model , enabling to drive the training efficiently.

In our last paper (a huge effort form Emmanouil Xypakis), we demonstrate how by introducing a modeling of light absorption (producing a Poissonian distribution of intensity) in the image formation, a neural network for designed for denoising, produces better results, especially in the high dynamic range regime: i.e. the network is capable to distinguish noise form signal both in the high intensity and low intensity part of the image.

We come out of this year long work with more expertise on NN and we will use this acquired know how to develop further and more sophisticated architectures for images enhancement.

A generative model to estimate large-scale connectivity in the Brain

To estimate how connections realize in the brain is an incredibly challenging task, for the lack of a massive instrument capable to follow the causal relations between different brain areas, neural columns and individual neurons.

Ina recently published paper on Neural Networks proposed a new model based on recurrent Hopfield neural networks shine new light on this challenging open problem, providing a new tool to determine brain’s connectivity.

Thanks to all authors and in particular Giorgio Gosti, Edoardo Milanetti, Viola Folli, and Giancarlo Ruocco.